# 2D Attentional Irregular Scene Text Recognizer

arXiv: https://arxiv.org/abs/1906.05708 · HTML: https://mlsanity.com/paper/1906.05708/

## Key facts

- Cited by 18 papers in the map (data through 7 Oct 2026).
- First posted on arXiv 13 Jun 2019.

## Built on by (most cited first, 18 of 18)

- [Read Like Humans: Autonomous, Bidirectional and Iterative Language Modeling for Scene Text Recognition](https://mlsanity.com/paper/2103.06495/) (2021)
- [From Two to One: A New Scene Text Recognizer with Visual Language Modeling Network](https://mlsanity.com/paper/2108.09661/) (2021)
- [ABINet++: Autonomous, Bidirectional and Iterative Language Modeling for Scene Text Spotting](https://mlsanity.com/paper/2211.10578/) (2022)
- [CDistNet: Perceiving Multi-Domain Character Distance for Robust Text Recognition](https://mlsanity.com/paper/2111.11011/) (2021)
- [MaskOCR: Text Recognition with Masked Encoder-Decoder Pretraining](https://mlsanity.com/paper/2206.00311/) (2022)
- [Primitive Representation Learning for Scene Text Recognition](https://mlsanity.com/paper/2105.04286/) (2021)
- [Linguistic More: Taking a Further Step toward Efficient and Accurate Scene Text Recognition](https://mlsanity.com/paper/2305.05140/) (2023)
- [Multimodal Semi-Supervised Learning for Text Recognition](https://mlsanity.com/paper/2205.03873/) (2022)
- [Towards the Unseen: Iterative Text Recognition by Distilling from Errors](https://mlsanity.com/paper/2107.12081/) (2021)
- [ICDAR 2021 Competition on Scientific Table Image Recognition to LaTeX](https://mlsanity.com/paper/2105.14426/) (2021)
- [Tables to LaTeX: structure and content extraction from scientific tables](https://mlsanity.com/paper/2210.17246/) (2022)
- [MRN: Multiplexed Routing Network for Incremental Multilingual Text Recognition](https://mlsanity.com/paper/2305.14758/) (2023)
- [I2C2W: Image-to-Character-to-Word Transformers for Accurate Scene Text Recognition](https://mlsanity.com/paper/2105.08383/) (2021)
- [Visual-Semantic Transformer for Scene Text Recognition](https://mlsanity.com/paper/2112.00948/) (2021)
- [Parallel Scale-wise Attention Network for Effective Scene Text Recognition](https://mlsanity.com/paper/2104.12076/) (2021)
- [Augmented Transformers with Adaptive n-grams Embedding for Multilingual Scene Text Recognition](https://mlsanity.com/paper/2302.14261/) (2023)
- [License Plate Recognition Based On Multi-Angle View Model](https://mlsanity.com/paper/2309.12972/) (2023)
- [DOTA: Deformable Optimized Transformer Architecture for End-to-End Text Recognition with Retrieval-Augmented Generation](https://mlsanity.com/paper/2505.04175/) (2025)

## Abstract (arXiv)

Irregular scene text, which has complex layout in 2D space, is challenging to most previous scene text recognizers. Recently, some irregular scene text recognizers either rectify the irregular text to regular text image with approximate 1D layout or transform the 2D image feature map to 1D feature sequence. Though these methods have achieved good performance, the robustness and accuracy are still limited due to the loss of spatial information in the process of 2D to 1D transformation. Different from all of previous, we in this paper propose a framework which transforms the irregular text with 2D layout to character sequence directly via 2D attentional scheme. We utilize a relation attention module to capture the dependencies of feature maps and a parallel attention module to decode all characters in parallel, which make our method more effective and efficient. Extensive experiments on several public benchmarks as well as our collected multi-line text dataset show that our approach is effective to recognize regular and irregular scene text and outperforms previous methods both in accuracy and speed.